diff --git a/dlt/common/exceptions.py b/dlt/common/exceptions.py index efb0877ba2..aa987f6766 100644 --- a/dlt/common/exceptions.py +++ b/dlt/common/exceptions.py @@ -12,15 +12,15 @@ def __init__(self, method: str) -> None: super().__init__(f"Process pool supports only fork start method, {method} not supported. Switch the pool type to threading") -class CannotInstallDependency(DltException): - def __init__(self, dependency: str, interpreter: str, output: AnyStr) -> None: - self.dependency = dependency +class CannotInstallDependencies(DltException): + def __init__(self, dependencies: Sequence[str], interpreter: str, output: AnyStr) -> None: + self.dependencies = dependencies self.interpreter = interpreter if isinstance(output, bytes): str_output = output.decode("utf-8") else: str_output = output - super().__init__(f"Cannot install dependency {dependency} with {interpreter} and pip:\n{str_output}\n") + super().__init__(f"Cannot install dependencies {', '.join(dependencies)} with {interpreter} and pip:\n{str_output}\n") class VenvNotFound(DltException): diff --git a/dlt/common/runners/venv.py b/dlt/common/runners/venv.py index e4e8532248..d81e7384b4 100644 --- a/dlt/common/runners/venv.py +++ b/dlt/common/runners/venv.py @@ -6,7 +6,7 @@ import subprocess from typing import Any, List, Type -from dlt.common.exceptions import CannotInstallDependency, VenvNotFound +from dlt.common.exceptions import CannotInstallDependencies, VenvNotFound class DLTEnvBuilder(venv.EnvBuilder): @@ -59,8 +59,8 @@ def restore_current(cls) -> "Venv": venv = cls.restore(os.environ["VIRTUAL_ENV"], current=True) except KeyError: import sys - bin_path, _ = os.path.split(sys.executable) - context = types.SimpleNamespace(bin_path=bin_path, env_exe=sys.executable) + # do not set bin path because it is not known + context = types.SimpleNamespace(bin_path="", env_exe=sys.executable) venv = cls(context, current=True) return venv @@ -114,12 +114,12 @@ def add_dependencies(self, dependencies: List[str] = None) -> None: @staticmethod def _install_deps(context: types.SimpleNamespace, dependencies: List[str]) -> None: - for dep in dependencies: - cmd = [context.env_exe, "-Im", "pip", "install", dep] - try: - subprocess.check_output(cmd, stderr=subprocess.STDOUT) - except subprocess.CalledProcessError as exc: - raise CannotInstallDependency(dep, context.env_exe, exc.output) + cmd = [context.env_exe, "-Im", "pip", "install"] + try: + subprocess.check_output(cmd + dependencies, stderr=subprocess.STDOUT) + except subprocess.CalledProcessError as exc: + raise CannotInstallDependencies(dependencies, context.env_exe, exc.output) + @staticmethod def is_virtual_env() -> bool: diff --git a/dlt/common/runtime/json_logging.py b/dlt/common/runtime/json_logging.py new file mode 100644 index 0000000000..bfff063dab --- /dev/null +++ b/dlt/common/runtime/json_logging.py @@ -0,0 +1,179 @@ + +import logging +from datetime import datetime # noqa: I251 +import traceback +from logging import Logger +from typing import Any, List, Type + +from dlt.common.json import json +from dlt.common.typing import DictStrAny, StrAny + +EMPTY_VALUE = '-' +JSON_SERIALIZER = lambda log: json.dumps(log) +COMPONENT_ID = EMPTY_VALUE +COMPONENT_NAME = EMPTY_VALUE +COMPONENT_INSTANCE_INDEX = 0 + +# The list contains all the attributes listed in +# http://docs.python.org/library/logging.html#logrecord-attributes +RECORD_ATTR_SKIP_LIST = [ + 'asctime', 'created', 'exc_info', 'exc_text', 'filename', 'args', + 'funcName', 'id', 'levelname', 'levelno', 'lineno', 'module', 'msg', + 'msecs', 'msecs', 'message', 'name', 'pathname', 'process', + 'processName', 'relativeCreated', 'thread', 'threadName', 'extra', + # Also exclude legacy 'props' + 'props', +] + +RECORD_ATTR_SKIP_LIST.append('stack_info') +EASY_TYPES = (str, bool, dict, float, int, list, type(None)) + +_default_formatter: Type[logging.Formatter] = None +_epoch = datetime(1970, 1, 1) + + +def config_root_logger() -> None: + """ + You must call this if you are using root logger. + Make all root logger' handlers produce JSON format + & remove duplicate handlers for request instrumentation logging. + Please made sure that you call this after you called "logging.basicConfig() or logging.getLogger() + """ + global _default_formatter + update_formatter_for_loggers([logging.root], _default_formatter) + + +def init(custom_formatter: Type[logging.Formatter] = None) -> None: + """ + This is supposed to be called only one time. + + If **custom_formatter** is passed, it will (in non-web context) use this formatter over the default. + """ + + global _default_formatter + + if custom_formatter: + if not issubclass(custom_formatter, logging.Formatter): + raise ValueError('custom_formatter is not subclass of logging.Formatter', custom_formatter) + + _default_formatter = custom_formatter if custom_formatter else JSONLogFormatter + logging._defaultFormatter = _default_formatter() # type: ignore + + # go to all the initialized logger and update it to use JSON formatter + existing_loggers = list(map(logging.getLogger, logging.Logger.manager.loggerDict)) + update_formatter_for_loggers(existing_loggers, _default_formatter) + + +class BaseJSONFormatter(logging.Formatter): + """ + Base class for JSON formatters + """ + base_object_common: DictStrAny = {} + + def __init__(self, *args: Any, **kw: Any) -> None: + super(BaseJSONFormatter, self).__init__(*args, **kw) + if COMPONENT_ID and COMPONENT_ID != EMPTY_VALUE: + self.base_object_common["component_id"] = COMPONENT_ID + if COMPONENT_NAME and COMPONENT_NAME != EMPTY_VALUE: + self.base_object_common["component_name"] = COMPONENT_NAME + if COMPONENT_INSTANCE_INDEX and COMPONENT_INSTANCE_INDEX != EMPTY_VALUE: + self.base_object_common["component_instance_idx"] = COMPONENT_INSTANCE_INDEX + + def format(self, record: logging.LogRecord) -> str: # noqa + log_object = self._format_log_object(record) + return JSON_SERIALIZER(log_object) + + def _format_log_object(self, record: logging.LogRecord) -> DictStrAny: + utcnow = datetime.utcnow() + base_obj = { + "written_at": iso_time_format(utcnow), + "written_ts": epoch_nano_second(utcnow), + } + base_obj.update(self.base_object_common) + # Add extra fields + base_obj.update(self._get_extra_fields(record)) + return base_obj + + def _get_extra_fields(self, record: logging.LogRecord) -> StrAny: + fields: DictStrAny = {} + + if record.args: + fields['msg'] = record.msg + + for key, value in record.__dict__.items(): + if key not in RECORD_ATTR_SKIP_LIST: + if isinstance(value, EASY_TYPES): + fields[key] = value + else: + fields[key] = repr(value) + + # Always add 'props' to the root of the log, assumes props is a dict + if hasattr(record, 'props') and isinstance(record.props, dict): + fields.update(record.props) + + return fields + + + +def _sanitize_log_msg(record: logging.LogRecord) -> str: + return record.getMessage().replace('\n', '_').replace('\r', '_').replace('\t', '_') + + +class JSONLogFormatter(BaseJSONFormatter): + """ + Formatter for non-web application log + """ + + def get_exc_fields(self, record: logging.LogRecord) -> StrAny: + if record.exc_info: + exc_info = self.format_exception(record.exc_info) + else: + exc_info = record.exc_text + return { + 'exc_info': exc_info, + 'filename': record.filename, + } + + @classmethod + def format_exception(cls, exc_info: Any) -> str: + return ''.join(traceback.format_exception(*exc_info)) if exc_info else '' + + def _format_log_object(self, record: logging.LogRecord) -> DictStrAny: + json_log_object = super(JSONLogFormatter, self)._format_log_object(record) + json_log_object.update({ + "msg": _sanitize_log_msg(record), + "type": "log", + "logger": record.name, + "thread": record.threadName, + "level": record.levelname, + "module": record.module, + "line_no": record.lineno, + }) + + if record.exc_info or record.exc_text: + json_log_object.update(self.get_exc_fields(record)) + + return json_log_object + + +def update_formatter_for_loggers(loggers_iter: List[Logger], formatter: Type[logging.Formatter]) -> None: + """ + :param formatter: + :param loggers_iter: + """ + for logger in loggers_iter: + if not isinstance(logger, Logger): + raise RuntimeError("%s is not a logging.Logger instance", logger) + for handler in logger.handlers: + if not isinstance(handler.formatter, formatter): + handler.formatter = formatter() + + +def epoch_nano_second(datetime_: datetime) -> int: + return int((datetime_ - _epoch).total_seconds()) * 1000000000 + datetime_.microsecond * 1000 + + +def iso_time_format(datetime_: datetime) -> str: + return '%04d-%02d-%02dT%02d:%02d:%02d.%03dZ' % ( + datetime_.year, datetime_.month, datetime_.day, datetime_.hour, datetime_.minute, datetime_.second, + int(datetime_.microsecond / 1000)) diff --git a/dlt/common/runtime/logger.py b/dlt/common/runtime/logger.py index 14fa238d90..f833d36608 100644 --- a/dlt/common/runtime/logger.py +++ b/dlt/common/runtime/logger.py @@ -1,6 +1,5 @@ import contextlib import logging -import json_logging import traceback from logging import LogRecord, Logger from typing import Any, Iterator, Protocol @@ -85,17 +84,6 @@ def format(self, record: LogRecord) -> str: # noqa: A003 return s -class _CustomJsonFormatter(json_logging.JSONLogFormatter): - - version: StrStr = None - - def _format_log_object(self, record: LogRecord, request_util: Any) -> Any: - json_log_object = super(_CustomJsonFormatter, self)._format_log_object(record, request_util) - if self.version: - json_log_object.update({"version": self.version}) - return json_log_object - - def _init_logging(logger_name: str, level: str, fmt: str, component: str, version: StrStr) -> Logger: if logger_name == "root": logging.basicConfig(level=level) @@ -111,13 +99,24 @@ def _init_logging(logger_name: str, level: str, fmt: str, component: str, versio # set right formatter if is_json_logging(fmt): + from dlt.common.runtime import json_logging + + class _CustomJsonFormatter(json_logging.JSONLogFormatter): + version: StrStr = None + + def _format_log_object(self, record: LogRecord) -> Any: + json_log_object = super(_CustomJsonFormatter, self)._format_log_object(record) + if self.version: + json_log_object.update({"version": self.version}) + return json_log_object + json_logging.COMPONENT_NAME = component - json_logging.JSON_SERIALIZER = json.dumps - json_logging.RECORD_ATTR_SKIP_LIST.remove("process") + if "process" in json_logging.RECORD_ATTR_SKIP_LIST: + json_logging.RECORD_ATTR_SKIP_LIST.remove("process") # set version as class variable as we cannot pass custom constructor parameters _CustomJsonFormatter.version = version # the only thing method above effectively does is to replace the formatter - json_logging.init_non_web(enable_json=True, custom_formatter=_CustomJsonFormatter) + json_logging.init(custom_formatter=_CustomJsonFormatter) if logger_name == "root": json_logging.config_root_logger() else: diff --git a/dlt/common/schema/utils.py b/dlt/common/schema/utils.py index 74419eb336..98de8b881a 100644 --- a/dlt/common/schema/utils.py +++ b/dlt/common/schema/utils.py @@ -426,7 +426,7 @@ def diff_tables(tab_a: TTableSchema, tab_b: TPartialTableSchema) -> TPartialTabl continue existing_v = tab_a.get(k) if existing_v != v: - print(f"{k} ==? {v} ==? {existing_v}") + # print(f"{k} ==? {v} ==? {existing_v}") partial_table[k] = v # type: ignore # this should not really happen diff --git a/dlt/common/storages/filesystem.py b/dlt/common/storages/filesystem.py index f968c37b5f..ef670c784a 100644 --- a/dlt/common/storages/filesystem.py +++ b/dlt/common/storages/filesystem.py @@ -41,7 +41,12 @@ class FileItem(TypedDict): def filesystem(protocol: str, credentials: FileSystemCredentials = None) -> Tuple[AbstractFileSystem, str]: """Instantiates an authenticated fsspec `FileSystem` for a given `protocol` and credentials. - Please supply credentials instance corresponding to the protocol + Please supply credentials instance corresponding to the protocol. The `protocol` is just the code name of the filesystem ie: + * s3 + * az, abfs + * gcs, gs + + also see filesystem_from_config """ return filesystem_from_config(FilesystemConfiguration(protocol, credentials)) diff --git a/dlt/destinations/athena/configuration.py b/dlt/destinations/athena/configuration.py index d6ba5e3814..f6e6fa3b51 100644 --- a/dlt/destinations/athena/configuration.py +++ b/dlt/destinations/athena/configuration.py @@ -13,4 +13,11 @@ class AthenaClientConfiguration(DestinationClientDwhWithStagingConfiguration): athena_work_group: Optional[str] = None aws_data_catalog: Optional[str] = "awsdatacatalog" - __config_gen_annotations__: ClassVar[List[str]] = ["athena_work_group"] \ No newline at end of file + __config_gen_annotations__: ClassVar[List[str]] = ["athena_work_group"] + + def __str__(self) -> str: + """Return displayable destination location""" + if self.staging_config: + return str(self.staging_config.credentials) + else: + return "[no staging set]" diff --git a/dlt/destinations/motherduck/configuration.py b/dlt/destinations/motherduck/configuration.py index 6f95bfce50..5b3460cea2 100644 --- a/dlt/destinations/motherduck/configuration.py +++ b/dlt/destinations/motherduck/configuration.py @@ -2,6 +2,7 @@ from dlt.common.configuration import configspec from dlt.common.destination.reference import DestinationClientDwhWithStagingConfiguration +from dlt.common.exceptions import DestinationTerminalException from dlt.common.typing import TSecretValue from dlt.common.utils import digest128 from dlt.common.configuration.exceptions import ConfigurationValueError @@ -28,6 +29,16 @@ def _token_to_password(self) -> None: if self.query and "token" in self.query: self.password = TSecretValue(self.query.pop("token")) + def borrow_conn(self, read_only: bool) -> Any: + from duckdb import HTTPException + try: + return super().borrow_conn(read_only) + except HTTPException as http_ex: + if http_ex.status_code == 403 and 'Failed to download extension "motherduck"' in str(http_ex): + from importlib.metadata import version as pkg_version + raise MotherduckLocalVersionNotSupported(pkg_version("duckdb")) from http_ex + raise + def parse_native_representation(self, native_value: Any) -> None: super().parse_native_representation(native_value) self._token_to_password() @@ -50,3 +61,9 @@ def fingerprint(self) -> str: if self.credentials and self.credentials.password: return digest128(self.credentials.password) return "" + + +class MotherduckLocalVersionNotSupported(DestinationTerminalException): + def __init__(self, duckdb_version: str) -> None: + self.duckdb_version = duckdb_version + super().__init__(f"Looks like your local duckdb version ({duckdb_version}) is not supported by Motherduck") diff --git a/dlt/destinations/snowflake/configuration.py b/dlt/destinations/snowflake/configuration.py index a27116f3eb..9124fa4f9f 100644 --- a/dlt/destinations/snowflake/configuration.py +++ b/dlt/destinations/snowflake/configuration.py @@ -1,3 +1,6 @@ +import base64 +import binascii + from typing import Final, Optional, Any, Dict, ClassVar, List from sqlalchemy.engine import URL @@ -23,9 +26,20 @@ def _read_private_key(private_key: str, password: Optional[str] = None) -> bytes except ModuleNotFoundError as e: raise MissingDependencyException("SnowflakeCredentials with private key", dependencies=[f"{version.DLT_PKG_NAME}[snowflake]"]) from e + try: + # decode base64 encoded private key + private_key_decoded = base64.b64decode(private_key).decode('utf-8') + except binascii.Error: + # private key is not in base64 -> assume it's already provided in plain-text + private_key_decoded = private_key + except UnicodeDecodeError: + # private key cannot be decoded into utf-8 -> assume it's already provided in plain-text + private_key_decoded = private_key + pkey = serialization.load_pem_private_key( - private_key.encode(), password.encode() if password is not None else None, backend=default_backend() + private_key_decoded.encode(), password.encode() if password is not None else None, backend=default_backend() ) + return pkey.private_bytes( encoding=serialization.Encoding.DER, format=serialization.PrivateFormat.PKCS8, diff --git a/dlt/extract/decorators.py b/dlt/extract/decorators.py index fe74d61f15..744fd1069c 100644 --- a/dlt/extract/decorators.py +++ b/dlt/extract/decorators.py @@ -247,7 +247,7 @@ def resource( merge_key: TTableHintTemplate[TColumnNames] = None, selected: bool = True, spec: Type[BaseConfiguration] = None, - depends_on: TUnboundDltResource = None, + data_from: TUnboundDltResource = None, ) -> Any: """When used as a decorator, transforms any generator (yielding) function into a `dlt resource`. When used as a function, it transforms data in `data` argument into a `dlt resource`. @@ -297,7 +297,7 @@ def resource( spec (Type[BaseConfiguration], optional): A specification of configuration and secret values required by the source. - depends_on (TUnboundDltResource, optional): Allows to pipe data from one resource to another to build multi-step pipelines. + data_from (TUnboundDltResource, optional): Allows to pipe data from one resource to another to build multi-step pipelines. Raises: ResourceNameMissing: indicates that name of the resource cannot be inferred from the `data` being passed. @@ -314,12 +314,12 @@ def make_resource(_name: str, _section: str, _data: Any, incremental: Incrementa primary_key=primary_key, merge_key=merge_key, ) - return DltResource.from_data(_data, _name, _section, table_template, selected, cast(DltResource, depends_on), incremental=incremental) + return DltResource.from_data(_data, _name, _section, table_template, selected, cast(DltResource, data_from), incremental=incremental) def decorator(f: Callable[TResourceFunParams, Any]) -> Callable[TResourceFunParams, DltResource]: if not callable(f): - if depends_on: + if data_from: # raise more descriptive exception if we construct transformer raise InvalidTransformerDataTypeGeneratorFunctionRequired(name or "", f, type(f)) raise ResourceFunctionExpected(name or "", f, type(f)) @@ -485,7 +485,7 @@ def transformer( # type: ignore merge_key=merge_key, selected=selected, spec=spec, - depends_on=data_from + data_from=data_from ) diff --git a/dlt/extract/source.py b/dlt/extract/source.py index d5748f3dd6..f28a54023d 100644 --- a/dlt/extract/source.py +++ b/dlt/extract/source.py @@ -64,7 +64,7 @@ def from_data( section: str = None, table_schema_template: TTableSchemaTemplate = None, selected: bool = True, - depends_on: Union["DltResource", Pipe] = None, + data_from: Union["DltResource", Pipe] = None, incremental: IncrementalResourceWrapper = None ) -> "DltResource": if data is None: @@ -95,9 +95,9 @@ def from_data( # check if depends_on is a valid resource parent_pipe: Pipe = None - if depends_on is not None: + if data_from is not None: DltResource._ensure_valid_transformer_resource(name, data) - parent_pipe = DltResource._get_parent_pipe(name, depends_on) + parent_pipe = DltResource._get_parent_pipe(name, data_from) # create resource from iterator, iterable or generator function if isinstance(data, (Iterable, Iterator)) or callable(data): @@ -779,8 +779,6 @@ def _add_resource(self, name: str, resource: DltResource) -> None: self._resources[name].add_pipe(resource) else: self._resources[name] = resource - # remember that resource got cloned when set into dict - super().__setattr__(name, self._resources[name]) def __getattr__(self, resource_name: str) -> DltResource: return self._resources[resource_name] diff --git a/dlt/helpers/dbt/__init__.py b/dlt/helpers/dbt/__init__.py index bef585386c..c6107b2873 100644 --- a/dlt/helpers/dbt/__init__.py +++ b/dlt/helpers/dbt/__init__.py @@ -43,18 +43,23 @@ def _create_dbt_deps(destination_names: List[str], dbt_version: str = DEFAULT_DB else: dbt_version = "" - all_packages = destination_names + ["core"] + # add version only to the core package. the other packages versions must be resolved by pip + all_packages = ["core" + dbt_version] + destination_names for idx, package in enumerate(all_packages): - # TODO: if we have more cases like this, move to destination capabilities - package = DBT_DESTINATION_MAP.get(package, package) - package_w_ver = "dbt-" + package + dbt_version + package = "dbt-" + DBT_DESTINATION_MAP.get(package, package) # verify package - pkg_resources.Requirement.parse(package_w_ver) - all_packages[idx] = package_w_ver + pkg_resources.Requirement.parse(package) + all_packages[idx] = package dlt_requirement = get_installed_requirement_string() - - return all_packages + [dlt_requirement] + # get additional requirements + additional_deps: List[str] = [] + if "duckdb" in destination_names or "motherduck" in destination_names: + from importlib.metadata import version as pkg_version + # force locally installed duckdb + additional_deps = ["duckdb" + "==" + pkg_version("duckdb")] + + return all_packages + additional_deps + [dlt_requirement] def restore_venv(venv_dir: str, destination_names: List[str], dbt_version: str = DEFAULT_DBT_VERSION) -> Venv: diff --git a/dlt/helpers/dbt/profiles.yml b/dlt/helpers/dbt/profiles.yml index cccab0d369..2414222cbd 100644 --- a/dlt/helpers/dbt/profiles.yml +++ b/dlt/helpers/dbt/profiles.yml @@ -122,7 +122,7 @@ snowflake_pkey: role: "{{ env_var('DLT__CREDENTIALS__ROLE', '') }}" schema: "{{ var('destination_dataset_name', var('source_dataset_name')) }}" warehouse: "{{ env_var('DLT__CREDENTIALS__WAREHOUSE', '') }}" - private_key: "{{ env_var('DLT__CREDENTIALS__PRIVATE_KEY') }}" + private_key: "{{ env_var('DLT__CREDENTIALS__PRIVATE_KEY') }}" # base64 encoded private key private_key_passphrase: "{{ env_var('DLT__CREDENTIALS__PRIVATE_KEY_PASSPHRASE', '') }}" threads: 4 diff --git a/docs/website/blog/2023-09-26-verba-dlt-zendesk.md b/docs/website/blog/2023-09-26-verba-dlt-zendesk.md index b79f862121..b075dd9163 100644 --- a/docs/website/blog/2023-09-26-verba-dlt-zendesk.md +++ b/docs/website/blog/2023-09-26-verba-dlt-zendesk.md @@ -14,7 +14,7 @@ tags: [Verba, dlt, Zendesk, RAG, Generative Search, Weaviate, OpenAI] As businesses scale and the volume of internal knowledge grows, it becomes increasingly difficult for everyone in the company to find the right information at the right time. -With the latest advancements in large language models (LLMs) and vector databases, it's now possible to build a new class of tools that can help get insights from this data. One approach to do so is Retrieval-Augmented Generation (RAG). The idea behind RAGs is to retrieve relevant information from your database and use LLMs to generate a customised response to a question. Leveraging RAG enables the LLM to tailor its responses based on your proprietary data. +With the latest advancements in large language models (LLMs) and [vector databases](https://weaviate.io/blog/what-is-a-vector-database), it's now possible to build a new class of tools that can help get insights from this data. One approach to do so is Retrieval-Augmented Generation (RAG). The idea behind RAGs is to retrieve relevant information from your database and use LLMs to generate a customised response to a question. Leveraging RAG enables the LLM to tailor its responses based on your proprietary data. ![Diagram illustrating the process of internal business knowledge retrieval and augmented generation (RAG), involving components like Salesforce, Zendesk, Asana, Jira, Notion, Slack and HubSpot, to answer user queries and generate responses.](/img/dlt-business-knowledge-retrieval-augmented-generation-diagram.png) diff --git a/docs/website/blog/2023-09-26-zendesk-to-verba.md b/docs/website/blog/2023-09-26-zendesk-to-verba.md deleted file mode 100644 index b075dd9163..0000000000 --- a/docs/website/blog/2023-09-26-zendesk-to-verba.md +++ /dev/null @@ -1,296 +0,0 @@ ---- -slug: verba-dlt-zendesk -title: "Talk to your Zendesk tickets with Weaviate’s Verba and dlt: A Step by Step Guide" -image: /img/dlt-business-knowledge-retrieval-augmented-generation-diagram.png -authors: - name: Anton Burnashev - title: Software Engineer - url: https://github.com/burnash - image_url: https://avatars.githubusercontent.com/u/264674?v=4 -tags: [Verba, dlt, Zendesk, RAG, Generative Search, Weaviate, OpenAI] ---- - -*tl;dr: In this blog post, we'll build a RAG chatbot for Zendesk Support data using Verba and dlt.* - -As businesses scale and the volume of internal knowledge grows, it becomes increasingly difficult for everyone in the company to find the right information at the right time. - -With the latest advancements in large language models (LLMs) and [vector databases](https://weaviate.io/blog/what-is-a-vector-database), it's now possible to build a new class of tools that can help get insights from this data. One approach to do so is Retrieval-Augmented Generation (RAG). The idea behind RAGs is to retrieve relevant information from your database and use LLMs to generate a customised response to a question. Leveraging RAG enables the LLM to tailor its responses based on your proprietary data. - -![Diagram illustrating the process of internal business knowledge retrieval and augmented generation (RAG), involving components like Salesforce, Zendesk, Asana, Jira, Notion, Slack and HubSpot, to answer user queries and generate responses.](/img/dlt-business-knowledge-retrieval-augmented-generation-diagram.png) - -One such source of internal knowledge is help desk software. It contains a wealth of information about the company's customers and their interactions with the support team. - -In this blog post, we'll guide you through the process of building a RAG application for Zendesk Support data, a popular help desk software. We’re going to use dlt, Weaviate, Verba and OpenAI. - -[**dlt**](https://github.com/dlt-hub/dlt) is an open-source Python library that simplifies the process of loading data from various sources. It does not requires extensive setup or maintenance and particularly useful for CRM data: highly tailored to the needs of the business and changes frequently. - -[**Weaviate**](https://weaviate.io/) is an open-source, AI-native vector database that is redefining the foundation of AI-powered applications. With capabilities for vector, hybrid, and generative search over billions of data objects, Weaviate serves as the critical infrastructure for organizations building sophisticated AI solutions and exceptional user experiences. - -[**Verba**](https://github.com/weaviate/Verba) is an open-source chatbot powered by Weaviate. It's built on top of Weaviate's state-of-the-art [Generative Search](https://weaviate.io/developers/weaviate/search/generative) technology. Verba includes a web interface and a query engine that uses Weaviate database. - -## Prerequisites - -1. A URL and an API key of a Weaviate instance. We're using the hosted version of Weaviate to store our data. Head over to the [Weaviate Cloud Services](https://console.weaviate.cloud/dashboard) and create a new cluster. You can create a free sandbox, but keep in mind your cluster will expire and your data will be deleted after 14 days. In the "Details" of your cluster you'll find the Cluster URL and the API key. -2. An OpenAI account and API key. Verba utilizes OpenAI's models to generate answers to user's questions and Weaviate uses them to [vectorize](https://weaviate.io/developers/weaviate/concepts/vector-index) text before storing it in the database. You can sign up for an account on [OpenAI's website](https://platform.openai.com/). -3. A Zendesk account and API credentials. - -## Let’s get started - -### Step 1. Set up Verba - -Create a new folder for your project and install Verba: - -```bash -mkdir verba-dlt-zendesk -cd verba-dlt-zendesk -python -m venv venv -source venv/bin/activate -pip install goldenverba -``` - -To configure Verba, we need to set the following environment variables: - -```bash -VERBA_URL=https://your-cluster.weaviate.network # your Weaviate instance URL -VERBA_API_KEY=F8...i4WK # the API key of your Weaviate instance -OPENAI_API_KEY=sk-...R # your OpenAI API key - -``` - -You can put them in a `.env` file in the root of your project or export them in your shell. - -Let's test that Verba is installed correctly: - -```bash -verba start -``` - -You should see the following output: - -```bash -INFO: Uvicorn running on (Press CTRL+C to quit) -ℹ Setting up client -✔ Client connected to Weaviate Cluster -INFO: Started server process [50252] -INFO: Waiting for application startup. -INFO: Application startup complete. -``` - -Now, open your browser and navigate to [http://localhost:8000](http://localhost:8000/). - -![A user interface screenshot showing Verba, retrieval and augmented generation chatbot, powered by Weaviate](/img/dlt-weaviate-verba-ui-1.png) - -Great! Verba is up and running. - -If you try to ask a question now, you'll get an error in return. That's because we haven't imported any data yet. We'll do that in the next steps. - -### Step 2. Install dlt with Zendesk source - -We get our data from Zendesk using dlt. Let's install it along with the Weaviate extra: - -```bash -pip install "dlt[weaviate]" -``` - -This also installs a handy CLI tool called `dlt`. It will help us initialize the [Zendesk verified data source](https://dlthub.com/docs/dlt-ecosystem/verified-sources/zendesk)—a connector to Zendesk Support API. - -Let's initialize the verified source: - -```bash -dlt init zendesk weaviate -``` - -`dlt init` pulls the latest version of the connector from the [verified source repository](https://github.com/dlt-hub/verified-sources) and creates a credentials file for it. The credentials file is called `secrets.toml` and it's located in the `.dlt` directory. - -To make things easier, we'll use the email address and password authentication method for Zendesk API. Let's add our credentials to `secrets.toml`: - -```yaml -[sources.zendesk.credentials] -password = "your-password" -subdomain = "your-subdomain" -email = "your-email@example.com" -``` - -We also need to specify the URL and the API key of our Weaviate instance. Copy the credentials for the Weaviate instance you created earlier and add them to `secrets.toml`: - -```yaml -[destination.weaviate.credentials] -url = "https://your-cluster.weaviate.network" -api_key = "F8.....i4WK" - -[destination.weaviate.credentials.additional_headers] -X-OpenAI-Api-Key = "sk-....." - -``` - -All the components are now in place and configured. Let's set up a pipeline to import data from Zendesk. - -### Step 3. Set up a dlt pipeline - -Open your favorite text editor and create a file called `zendesk_verba.py`. Add the following code to it: - -```python -import itertools - -import dlt -from weaviate.util import generate_uuid5 -from dlt.destinations.weaviate import weaviate_adapter - -from zendesk import zendesk_support - -def to_verba_document(ticket): - # The document id is the ticket id. - # dlt will use this to generate a UUID for the document in Weaviate. - return { - "doc_id": ticket["id"], - "doc_name": ticket["subject"], - "doc_type": "Zendesk ticket", - "doc_link": ticket["url"], - "text": ticket["description"], - } - -def to_verba_chunk(ticket): - # We link the chunk to the document by using the document (ticket) id. - return { - "chunk_id": 0, - "doc_id": ticket["id"], - "doc_name": ticket["subject"], - "doc_type": "Zendesk ticket", - "doc_uuid": generate_uuid5(ticket["id"], "Document"), - "text": ticket["description"], - } - -def main(): - pipeline = dlt.pipeline( - pipeline_name="zendesk_verba", - destination="weaviate", - ) - - # Zendesk Support has data tickets, users, groups, etc. - zendesk_source = zendesk_support(load_all=False) - - # Here we get a dlt resource containing only the tickets - tickets = zendesk_source.tickets - - # Split the tickets into two streams - tickets1, tickets2 = itertools.tee(tickets, 2) - - @dlt.resource(primary_key="doc_id", write_disposition="merge") - def document(): - # Map over the tickets and convert them to Verba documents - # primary_key is the field that will be used to generate - # a UUID for the document in Weaviate. - yield from weaviate_adapter( - map(to_verba_document, tickets1), - vectorize="text", - ) - - @dlt.resource(primary_key="doc_id", write_disposition="merge") - def chunk(): - yield from weaviate_adapter( - map(to_verba_chunk, tickets2), - vectorize="text", - ) - - info = pipeline.run([document, chunk]) - - return info - -if __name__ == "__main__": - load_info = main() - print(load_info) -``` - -There's a lot going on here, so let's break it down. - -First, in `main()` we create a dlt pipeline and add a Weaviate destination to it. We'll use it to store our data. - -Next, we create a Zendesk Support source. It will fetch data from Zendesk Support API. - -To match the data model of Zendesk Support to the internal data model of Verba, we need to convert Zendesk tickets to Verba documents and chunks. We do that by defining two functions: `to_verba_document` and `to_verba_chunk`. We also create two streams of tickets. We'll use them to create two dlt resources: `document` and `chunk`. These will populate the `Document` and `Chunk` classes in Verba. In both resources we instruct dlt which fields to vectorize using the `weaviate_adapter()` function. - -We specify `primary_key` and `write_disposition` for both resources. `primary_key` is the field that will be used to generate a UUID for the document in Weaviate. `write_disposition` tells dlt how to handle duplicate documents. In our case, we want to merge them: if a document already exists in Weaviate, we want to update it with the new data. - -Finally, we run the pipeline and print the load info. - -### Step 4. Load data into Verba - -Let's run the pipeline: - -```bash -python zendesk_verba.py -``` - -You should see the following output: - -```bash -Pipeline zendesk_verba completed in 8.27 seconds -1 load package(s) were loaded to destination weaviate and into dataset None -The weaviate destination used location to store data -Load package 1695726495.383148 is LOADED and contains no failed jobs - -``` - -Verba is now populated with data from Zendesk Support. However there are a couple of classes that need to be created in Verba: Cache and Suggestion. We can do that using the Verba CLI `init` command. When it runs it will ask us if we want to create Verba classes. Make sure to answer "n" to the question about the Document class — we don't want to overwrite it. - -Run the following command: - -```bash -verba init -``` - -You should see the following output: - -```bash -===================== Creating Document and Chunk class ===================== -ℹ Setting up client -✔ Client connected to Weaviate Cluster -Document class already exists, do you want to overwrite it? (y/n): n -⚠ Skipped deleting Document and Chunk schema, nothing changed -ℹ Done - -============================ Creating Cache class ============================ -ℹ Setting up client -✔ Client connected to Weaviate Cluster -✔ 'Cache' schema created -ℹ Done - -========================= Creating Suggestion class ========================= -ℹ Setting up client -✔ Client connected to Weaviate Cluster -✔ 'Suggestion' schema created -ℹ Done -``` - -We're almost there! Let's start Verba: - -```bash -verba start -``` - -### Step 4. Ask Verba a question - -Head back to [http://localhost:8000](http://localhost:8000/) and ask Verba a question. For example, "What are common issues our users report?". - -![A user interface screenshot of Verba showing Zendesk tickets with different issues like API problems and update failures, with responses powered by Weaviate](/img/dlt-weaviate-verba-ui-2.png) - -As you can see, Verba is able to retrieve relevant information from Zendesk Support and generate an answer to our question. It also displays the list of relevant documents for the question. You can click on them to see the full text. - -## Conclusion - -In this blog post, we've built a RAG application for Zendesk Support using Verba and dlt. We've learned: - -- How easy it is to get started with Verba. -- How to build dlt pipeline with a ready-to-use data source. -- How to customize the pipeline so it matches the data model of Verba. - -## Where to go next? - -- **Ensure your data is up-to-date.** With `dlt deploy` you can [deploy your pipeline](https://dlthub.com/docs/walkthroughs/deploy-a-pipeline) to Google's Cloud Composer or GitHub Actions and run it on a schedule. -- **Build a Verba RAG for other data sources.** Interested in building a RAG that queries other internal business knowledge than Zendesk? With dlt you can easily switch your data source. Other dlt verified sources of internal business knowledge include [Asana](https://dlthub.com/docs/dlt-ecosystem/verified-sources/asana), [Hubspot](https://dlthub.com/docs/dlt-ecosystem/verified-sources/hubspot), [Jira](https://dlthub.com/docs/dlt-ecosystem/verified-sources/jira), [Notion](https://dlthub.com/docs/dlt-ecosystem/verified-sources/notion), [Slack](https://github.com/dlt-hub/verified-sources/tree/master/sources/slack) and [Salesforce](https://dlthub.com/docs/dlt-ecosystem/verified-sources/salesforce). However, dlt isn’t just about ready-to-use data sources; many of our users choose to implement [their own custom data sources](https://dlthub.com/docs/build-a-pipeline-tutorial#why-build-pipelines-with-dlt). -- **Learn more about how Weaviate works.** Check out [Zero to MVP](https://weaviate.io/developers/academy/zero_to_mvp) course to learn more about Weaviate database and how to use it to build your own applications. -- **Request more features**. A careful reader might have noticed that we used both Document and Chunk classes in Verba for the same type of data. For simplicity's sake, we assumed that the ticket data is small enough to fit into a single chunk. However, if you if you're dealing with larger documents, you might consider splitting them into chunks. Should we add chunking support to dlt? Or perhaps you have other feature suggestions? If so, please consider opening a feature request in the [dlt repo](https://github.com/dlt-hub/dlt/issues) to discuss your ideas! - -## Let's stay in touch - -If you have any questions or feedback, please reach out to us on the [dltHub Slack](https://join.slack.com/t/dlthub-community/shared_invite/zt-1slox199h-HAE7EQoXmstkP_bTqal65g). diff --git a/docs/website/docs/dlt-ecosystem/destinations/snowflake.md b/docs/website/docs/dlt-ecosystem/destinations/snowflake.md index 788c5344c0..47d3aabf26 100644 --- a/docs/website/docs/dlt-ecosystem/destinations/snowflake.md +++ b/docs/website/docs/dlt-ecosystem/destinations/snowflake.md @@ -78,24 +78,21 @@ You can also pass credentials as a database connection string. For example: destination.snowflake.credentials="snowflake://loader:@kgiotue-wn98412/dlt_data?warehouse=COMPUTE_WH&role=DLT_LOADER_ROLE" ``` -In **key pair authentication** you replace password with a private key exported in PEM format. The key may be encrypted. In that case you must provide a passphrase. +In **key pair authentication** you replace password with a private key string that should be in Base64-encoded DER format ([DBT also recommends](https://docs.getdbt.com/docs/core/connect-data-platform/snowflake-setup#key-pair-authentication) base64-encoded private keys for Snowflake connections). The private key may also be encrypted. In that case you must provide a passphrase alongside with the private key. ```toml [destination.snowflake.credentials] database = "dlt_data" username = "loader" host = "kgiotue-wn98412" -private_key = """-----BEGIN ENCRYPTED PRIVATE KEY----- - MIIEvAIBADANBgkqhkiG9w0BAQEFAASCBKYwggSiAgEAAoIBAQDz5LZoccgKZ4jH - ... ------END PRIVATE KEY----- +private_key = "LS0tLS1CRUdJTiBFTkNSWVBURUQgUFJJ....Qo=" private_key_passphrase="passphrase" -""" ``` +> You can easily get the base64-encoded value of your private key by running `base64 -i .pem` in your terminal -We allow to pass private key and passphrase in connection string. Please url encode the private key and passphrase. +If you pass a passphrase in the connection string, please url encode it. ```toml # keep it at the top of your toml file! before any section starts -destination.snowflake.credentials="snowflake://loader:@kgiotue-wn98412/dlt_data?private_key=&private_key_passphrase=" +destination.snowflake.credentials="snowflake://loader:@kgiotue-wn98412/dlt_data?private_key=&private_key_passphrase=" ``` ## Write disposition diff --git a/docs/website/docs/general-usage/resource.md b/docs/website/docs/general-usage/resource.md index fa6a3832d3..305eb29da7 100644 --- a/docs/website/docs/general-usage/resource.md +++ b/docs/website/docs/general-usage/resource.md @@ -193,7 +193,7 @@ def users(limit=None): # feed data from users as user_item below, # all transformers must have at least one # argument that will receive data from the parent resource -@transformer(data_from=users) +@dlt.transformer(data_from=users) def users_details(user_item): for detail in _get_details(user_item["user_id"]): yield detail diff --git a/docs/website/requirements.txt b/docs/website/requirements.txt index f3b3cbcadf..5c14700f58 100644 --- a/docs/website/requirements.txt +++ b/docs/website/requirements.txt @@ -1 +1 @@ -pydoc-markdown \ No newline at end of file +pydoc-markdown==4.8.2 diff --git a/poetry.lock b/poetry.lock index 3266cbfb0d..5c852e7ed6 100644 --- a/poetry.lock +++ b/poetry.lock @@ -1,26 +1,18 @@ -# This file is automatically @generated by Poetry 1.6.1 and should not be changed by hand. - 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Implements apache-airflow-providers-common-sql package" +category = "dev" optional = false python-versions = "~=3.8" -files = [ - {file = "apache-airflow-providers-common-sql-1.7.1.tar.gz", hash = "sha256:ba37f795d9656a87cf4661edc381b8ecfe930272c59324b59f8a158fd0971aeb"}, - {file = "apache_airflow_providers_common_sql-1.7.1-py3-none-any.whl", hash = "sha256:36da2f51b51a64765b0ed5e6a5fece8eaa3ca173dfbff803e2fe2a0afbb90944"}, -] [package.dependencies] apache-airflow = ">=2.4.0" @@ -514,12 +388,9 @@ pandas = ["pandas (>=0.17.1)"] name = "apache-airflow-providers-ftp" version = "3.5.1" description = "Provider for Apache Airflow. 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-[package.dependencies] -idna = ">=2.0" -multidict = ">=4.0" - -[[package]] -name = "zipp" -version = "3.16.2" -description = "Backport of pathlib-compatible object wrapper for zip files" -optional = false -python-versions = ">=3.8" -files = [ +zipp = [ {file = "zipp-3.16.2-py3-none-any.whl", hash = "sha256:679e51dd4403591b2d6838a48de3d283f3d188412a9782faadf845f298736ba0"}, {file = "zipp-3.16.2.tar.gz", hash = "sha256:ebc15946aa78bd63458992fc81ec3b6f7b1e92d51c35e6de1c3804e73b799147"}, ] - -[package.extras] -docs = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-lint"] -testing = ["big-O", "jaraco.functools", "jaraco.itertools", "more-itertools", "pytest (>=6)", "pytest-black (>=0.3.7)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-ignore-flaky", "pytest-mypy (>=0.9.1)", "pytest-ruff"] - -[extras] -athena = ["botocore", "pyarrow", "pyathena", "s3fs"] -az = ["adlfs"] -bigquery = ["gcsfs", "google-cloud-bigquery", "grpcio", "pyarrow"] -cli = ["cron-descriptor", "pipdeptree"] -dbt = ["dbt-athena-community", "dbt-bigquery", "dbt-core", "dbt-duckdb", "dbt-redshift", "dbt-snowflake"] -duckdb = ["duckdb"] -filesystem = ["botocore", "s3fs"] -gcp = ["gcsfs", "google-cloud-bigquery", "grpcio"] -gs = ["gcsfs"] -motherduck = ["duckdb", "pyarrow"] -mssql = ["pyodbc"] -parquet = ["pyarrow"] -postgres = ["psycopg2-binary", "psycopg2cffi"] -pydantic = ["pydantic"] -redshift = ["psycopg2-binary", "psycopg2cffi"] -s3 = ["botocore", "s3fs"] -snowflake = ["snowflake-connector-python"] -weaviate = ["weaviate-client"] - -[metadata] -lock-version = "2.0" -python-versions = ">=3.8.1,<4.0" -content-hash = "91cf748b97044c958bdabb5bb6d0c9a75f78f2fb973e86620a06f34d237b7887" diff --git a/pyproject.toml b/pyproject.toml index 2d0219f530..cfc7b8a3eb 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -27,7 +27,6 @@ requests = ">=2.26.0" pendulum = ">=2.1.2" simplejson = ">=3.17.5" PyYAML = ">=5.4.1" -json-logging = "1.4.1rc0" semver = ">=2.13.0" hexbytes = ">=0.2.2" tzdata = ">=2022.1" diff --git a/tests/common/cases/secrets/encrypted-private-key-base64 b/tests/common/cases/secrets/encrypted-private-key-base64 new file mode 100644 index 0000000000..374021bd5d --- /dev/null +++ b/tests/common/cases/secrets/encrypted-private-key-base64 @@ -0,0 +1 @@ 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 diff --git a/tests/common/runners/test_venv.py b/tests/common/runners/test_venv.py index cd4f2726a4..79e485862e 100644 --- a/tests/common/runners/test_venv.py +++ b/tests/common/runners/test_venv.py @@ -5,7 +5,7 @@ import pytest import shutil -from dlt.common.exceptions import CannotInstallDependency +from dlt.common.exceptions import CannotInstallDependencies from dlt.common.runners import Venv, VenvNotFound from dlt.common.utils import custom_environ @@ -65,9 +65,9 @@ def test_create_with_dependency() -> None: def test_create_with_wrong_dependency() -> None: - with pytest.raises(CannotInstallDependency) as cid: + with pytest.raises(CannotInstallDependencies) as cid: Venv.create(tempfile.mkdtemp(), ["six", "_six_"]) - assert cid.value.dependency == "_six_" + assert cid.value.dependencies == ["six", "_six_"] def test_add_dependency() -> None: diff --git a/tests/common/runtime/test_logging.py b/tests/common/runtime/test_logging.py index 080a7bf3e4..4e6f9926bb 100644 --- a/tests/common/runtime/test_logging.py +++ b/tests/common/runtime/test_logging.py @@ -1,9 +1,9 @@ import pytest -import json_logging from importlib.metadata import version as pkg_version from dlt.common import logger from dlt.common.runtime import exec_info +from dlt.common.runtime.logger import is_logging from dlt.common.typing import StrStr from dlt.common.configuration import configspec from dlt.common.configuration.specs import RunConfiguration @@ -69,11 +69,13 @@ def test_text_logger_init(environment: StrStr) -> None: @pytest.mark.forked def test_json_logger_init(environment: StrStr) -> None: + from dlt.common.runtime import json_logging + mock_image_env(environment) mock_pod_env(environment) init_test_logging(JsonLoggerConfiguration()) # correct component was set - json_logging.COMPONENT_NAME = "logger" + assert json_logging.COMPONENT_NAME == "logger" logger.metrics("test health", extra={"metrics": "props"}) logger.metrics("test", extra={"metrics": "props"}) logger.warning("Warning message here") @@ -83,6 +85,34 @@ def test_json_logger_init(environment: StrStr) -> None: logger.exception("DIV") +@pytest.mark.forked +def test_double_log_init(environment: StrStr) -> None: + + mock_image_env(environment) + mock_pod_env(environment) + + # logging is enabled somewhere earlier... + # assert not is_logging() + # from regular logger + init_test_logging(PureBasicConfiguration()) + assert is_logging() + # caplog does not capture the formatted output of loggers below + # so I'm not able to test the exact output + # comment out @pytest.mark.forked and use -s option to see the log messages + # logger.LOGGER.propagate = True + logger.error("test warning", extra={"metrics": "props"}) + # normal logger + # to json + init_test_logging(JsonLoggerConfiguration()) + logger.error("test json warning", extra={"metrics": "props"}) + # to regular + init_test_logging(PureBasicConfiguration()) + logger.error("test warning", extra={"metrics": "props"}) + # to json + init_test_logging(JsonLoggerConfiguration()) + logger.error("test warning", extra={"metrics": "props"}) + + def test_cleanup(environment: StrStr) -> None: # this must happen after all forked tests (problems with tests teardowns in other tests) pass diff --git a/tests/extract/test_sources.py b/tests/extract/test_sources.py index 9a43c6ff39..6e72a9a014 100644 --- a/tests/extract/test_sources.py +++ b/tests/extract/test_sources.py @@ -1,4 +1,6 @@ import itertools +from typing import Iterator + import pytest import dlt @@ -709,6 +711,23 @@ def test_source(no_resources): s.resource_30 +def test_source_resource_attrs_with_conflicting_attrs() -> None: + """Resource names that conflict with DltSource attributes do not work with attribute access""" + dlt.pipeline(full_refresh=True) # Create pipeline so state property can be accessed + names = ["state", "resources", "schema", "name", "clone"] + @dlt.source + def test_source() -> Iterator[DltResource]: + for name in names: + yield dlt.resource(["A"], name=name) + + s = test_source() + + # Resources are in resource dict but attributes are not created + assert set(s.resources.keys()) == set(names) + for name in names: + assert not isinstance(getattr(s, name), DltResource) + + def test_add_transform_steps() -> None: # add all step types, using indexes. final steps # gen -> map that converts to str and multiplies character -> filter str of len 2 -> yield all characters in str separately diff --git a/tests/helpers/dbt_tests/test_runner_dbt_versions.py b/tests/helpers/dbt_tests/test_runner_dbt_versions.py index 9a43489688..ed6bbf8612 100644 --- a/tests/helpers/dbt_tests/test_runner_dbt_versions.py +++ b/tests/helpers/dbt_tests/test_runner_dbt_versions.py @@ -59,19 +59,20 @@ def dbt_package_f(request: Any) -> Iterator[Tuple[str, AnyFun]]: def test_infer_venv_deps() -> None: - requirements = _create_dbt_deps(["postgres", "bigquery"]) - assert requirements[:3] == [f"dbt-postgres{DEFAULT_DBT_VERSION}", f"dbt-bigquery{DEFAULT_DBT_VERSION}", f"dbt-core{DEFAULT_DBT_VERSION}"] + requirements = _create_dbt_deps(["postgres", "mssql"]) + assert requirements[:3] == [f"dbt-core{DEFAULT_DBT_VERSION}", "dbt-postgres", "dbt-sqlserver"] # should lead to here assert os.path.isdir(requirements[-1]) # provide exact version requirements = _create_dbt_deps(["postgres"], dbt_version="3.3.3") - assert requirements[:-1] == ["dbt-postgres==3.3.3", "dbt-core==3.3.3"] - # provide version range + assert requirements[:-1] == ["dbt-core==3.3.3", "dbt-postgres"] + # provide version ranges requirements = _create_dbt_deps(["duckdb"], dbt_version=">3") - assert requirements[:-1] == ["dbt-duckdb>3", "dbt-core>3"] + # special duckdb dependency + assert requirements[:-1] == ["dbt-core>3", "dbt-duckdb", "duckdb==0.8.1"] # we do not validate version ranges, pip will do it and fail when creating venv - requirements = _create_dbt_deps(["duckdb"], dbt_version="y") - assert requirements[:-1] == ["dbt-duckdby", "dbt-corey"] + requirements = _create_dbt_deps(["motherduck"], dbt_version="y") + assert requirements[:-1] == ["dbt-corey", "dbt-duckdb", "duckdb==0.8.1"] def test_default_profile_name() -> None: diff --git a/tests/load/snowflake/test_snowflake_configuration.py b/tests/load/snowflake/test_snowflake_configuration.py index 7214574f2d..e097575936 100644 --- a/tests/load/snowflake/test_snowflake_configuration.py +++ b/tests/load/snowflake/test_snowflake_configuration.py @@ -15,7 +15,7 @@ def test_connection_string_with_all_params() -> None: - url = "snowflake://user1:pass1@host1/db1?warehouse=warehouse1&role=role1&private_key=pk&private_key_passphrase=paphr" + url = "snowflake://user1:pass1@host1/db1?warehouse=warehouse1&role=role1&private_key=cGs%3D&private_key_passphrase=paphr" creds = SnowflakeCredentials() creds.parse_native_representation(url) @@ -26,7 +26,7 @@ def test_connection_string_with_all_params() -> None: assert creds.host == "host1" assert creds.warehouse == "warehouse1" assert creds.role == "role1" - assert creds.private_key == "pk" + assert creds.private_key == "cGs=" assert creds.private_key_passphrase == "paphr" expected = make_url(url) @@ -61,6 +61,31 @@ def test_to_connector_params() -> None: role='role1', ) + pkey_str = Path('./tests/common/cases/secrets/encrypted-private-key-base64').read_text('utf8') + + creds = SnowflakeCredentials() + creds.private_key = pkey_str # type: ignore[assignment] + creds.private_key_passphrase = '12345' # type: ignore[assignment] + creds.username = 'user1' + creds.database = 'db1' + creds.host = 'host1' + creds.warehouse = 'warehouse1' + creds.role = 'role1' + + params = creds.to_connector_params() + + assert isinstance(params['private_key'], bytes) + params.pop('private_key') + + assert params == dict( + user='user1', + database='db1', + account='host1', + password=None, + warehouse='warehouse1', + role='role1', + ) + def test_snowflake_credentials_native_value(environment) -> None: with pytest.raises(ConfigurationValueError):